Patient-Specific Virtual Endovascular Treatment Model
Bibliographic record
Abstract
In recent years, virtual endovascular treatment models introduced to assist endovascular interventionists in preoperatively assessing procedures’ feasibility, efficacy, and safety. The development of numerical biomechanical methods is a promising tool for accurately anticipating the interactions between tissue structures and medical devices, which could be used to evaluate potential risks and complications of a given procedure. The presented virtual endovascular treatment model introduces an efficient method that can be adjusted for patients with different anatomical and physiological features. The advantages of the current model include its capacity to recreate vascular wall deformability and the validation process of this model against real-time treatment results. The maximum vascular displacements recorded during stent deployment and after removing the delivery system were 8.20 mm and 4.80 mm, respectively. These displacements resulted from the deformation of the vascular structure during virtual treatment. The vascular deformation was observed in real-time patient procedures with a strong correlation with our results. Therefore, the current virtual endovascular treatment model is reliable with the predictability of vascular tissue deformation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".